Generative and agentic AI behave differently once embedded in real systems. This session examines what changes when AI moves beyond experimentation and begins influencing workflows, decisions, and execution. The discussion covers common failure modes in operational AI systems, the risks introduced by agentic behavior, and the design considerations required for control, observability, and recovery. It also looks at how to integrate AI into existing systems without disrupting delivery or creating long-term technical debt. Key takeaways: → How and where generative and agentic AI systems fail in real environments → The risks agentic behavior introduces and how to design for oversight → Practical patterns for control, monitoring, and recovery